This paper proposes a method that utilizes two stages of deep learning networks for segmentation and recognition of spinal vertebrae. The first stage involves a y-shaped model that learns the basic shape of vertebrae to segment the spine. The second stage determines the order of each vertebra in the spine. This two-stage method provides more reliable spine segmentation results.

This paper presented a video moving object segmentation and tracking system based on the active contour and the color classification models. First, the active contour model is applied to segment the target object in the initial frame. From the segmented object, the object and background regions are extracted. Then the object and the background regions are separately clustered according to color feature by using the K-means algorithm. Subsequently, the video object in the next frame is automatically tracked by using temporal differencing and block matching. The moving and stationary regions in a frame are estimated by the temporal differencing. In the moving regions, pixels are obtained their classification from the previous frame using block matching while they are directly received their classification from the previous frame in the stationary regions. Experimental results show that the proposed method provides better performance than the active contour method applied in video object tracking.

In this paper, an optical cryptosystem with adaptive steganography is proposed for color image encryption and decryption. The optical cryptosystem employs a double random phase encoding algorithm to encrypt and decrypt color images. The color image is first separated into three channels: red, green, and blue. Each channel is encrypted by two random phase masks generated from session keys. For higher security, an asymmetric method is applied to cipher these session keys. The ciphered data produced by the asymmetric method is then embedded into the encrypted color image by a content-dependent and low distortion data embedding technique. The key delivery is accomplished by hiding ciphered data into the encrypted color image with a specific hiding sequence generated by the zero-LSB sorting technique. Experimental results show that the proposed adaptive steganography-based cryptosystem has a good performance when it is applied to color images.

For object segmentation, traditional snake algorithms often require human interaction; region growing methods are considerably dependent on the selected homogeneity criterion and initial seeds; watershed algorithms, however, have the drawback of over segmentation. A new downstream algorithm based on a proposed extended gradient vector flow (E-GVF) field model is presented in this paper for multiobject segmentation. The proposed flow field, on one hand, diffuses and propagates gradients near object boundaries to provide an effective guiding force and, on the other hand, presents a higher resolution of direction than traditional GVF field. The downstream process starts with a set of seeds scored and selected by considering local gradient direction information around each pixel. This step is automatic and requires no human interaction, making our algorithm more suitable for practical applications. Experiments show that our algorithm is noise resistant and has the advantage of segmenting objects that are separated from the background, while ignoring the internal structures of them. We have tested the proposed algorithm with several realistic images (e.g., medical and complex background images) and gained good results.

For image segmentation, traditional snake algorithms are often short of the requirement of human interaction and capability in processing multiple objects simultaneously. Watershed techniques however have the drawback of over-segmentation. A new region growing algorithm based on the extended gradient vector flow (E-GVF) field model is proposed for multiple object segmentation. The proposed force field propagates gradient information of object boundaries and provides a good feature for region growing. We perform scoring and selection of seeds by considering their local gradient direction information. This step is automatic and requires no human interaction, making our algorithm suitable for applications. Experiments show that our algorithm is noise-resistant and also resolves the abovementioned drawbacks for snakes and watershed methods. We have tested our algorithm in segmenting multiple objects from realistic and even medical CT images and gained good results.